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A BART-based hierarchical approach using golden-summary-driven document shortening improves Vietnamese multi-document abstractive summarization.

2026-06-26 20:37 UTC pith:IKMNEVRR

load-bearing objection The golden-summary shortening step creates a training-inference gap that undercuts the reported ROUGE score and leaves the work too thin on details for serious evaluation. the 2 major comments →

arxiv 2606.19591 v1 pith:IKMNEVRR submitted 2026-06-17 cs.CL cs.AI

A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization

Vu Nguyen Nguyen Xuan , Huy Ngo Quang This is my paper
classification cs.CL cs.AI
keywords Vietnamesemulti-document summarizationabstractive summarizationBARThierarchical strategyVLSPdocument shortening
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes a method for Vietnamese multi-document abstractive summarization by first shortening individual documents using information from the reference summary, then aggregating and summarizing the condensed versions with a BART model. This hierarchical strategy aims to maintain high correlation between the condensation and final summarization stages. The approach also incorporates additional data from external sources to increase training data volume. A sympathetic reader would care because multi-document summarization in Vietnamese is a challenging task with limited resources, and this method demonstrates a way to achieve fluent outputs.

Core claim

The authors establish that their BART-based model with a hierarchical strategy, where documents are shortened in a manner driven by the golden summary, reaches a ROUGE2-F1 score of 0.2468 on the VLSP public test set while generating fluent and concise summaries. They further show that augmenting the dataset with external sources significantly increases the available training data for this task.

What carries the argument

The hierarchical approach of condensing each document before aggregation and summarization, with the condensation step guided by the golden summary to ensure relevance.

Load-bearing premise

The document shortening strategy assumes that golden reference summaries are available at training time to select which content to keep.

What would settle it

A controlled experiment where the model is trained and tested using only automatically generated or no-reference shortening and the ROUGE2-F1 score is compared to the reported value.

Watch this falsifier — get emailed when new claim-graph text bears on it.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper proposes a hierarchical BART-based approach for Vietnamese abstractive multi-document summarization. It introduces a novel strategy to shorten documents driven by the golden summary to ensure high correlation between the condensation and aggregation stages. The method achieves a ROUGE2-F1 score of 0.2468 on the VLSP 2022 public test set and releases additional data from external sources for the community.

Significance. If the reported performance holds without reference leakage in the pipeline, this work provides a practical method and additional resources for Vietnamese MDS, which is an under-resourced area. The hierarchical approach is standard, but the specific shortening strategy could be a contribution if properly validated. However, the absence of baselines, ablations, and detailed training information makes it difficult to assess the significance of the result.

major comments (2)
  1. [Proposed Method] The document-shortening strategy driven by the golden summary (described in the hierarchical approach section) requires reference summaries at training time. This risks introducing leakage into the condensed documents used for the second-stage BART aggregator, as the shortening enforces correlation using information unavailable at inference. The central ROUGE2-F1 claim of 0.2468 depends on this construction, which may not generalize to standard settings without references.
  2. [Experiments] No baselines, ablation studies, error analysis, or comparisons to other methods are reported despite the claim of effectiveness for the hierarchical strategy. Without these, the ROUGE2-F1 score of 0.2468 cannot be contextualized and the contribution of the proposed shortening method remains unverifiable.
minor comments (1)
  1. The abstract states that external sources are used for extra data but provides no details on collection, filtering, or how the additional data is integrated into training.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our technical report. We address each major comment below and commit to revisions that clarify the method and strengthen the experimental section.

read point-by-point responses

  1. Referee: [Proposed Method] The document-shortening strategy driven by the golden summary (described in the hierarchical approach section) requires reference summaries at training time. This risks introducing leakage into the condensed documents used for the second-stage BART aggregator, as the shortening enforces correlation using information unavailable at inference. The central ROUGE2-F1 claim of 0.2468 depends on this construction, which may not generalize to standard settings without references.

    Authors: We acknowledge the concern regarding potential reference leakage. The golden-summary-driven shortening is used only at training time to construct condensed documents that exhibit high overlap with the target summaries, thereby providing stronger supervision signals for the second-stage aggregator. At inference, document shortening is performed in a reference-free manner using the first-stage BART model itself to select and condense content. We agree that the original manuscript does not sufficiently distinguish these phases or validate the inference procedure. In revision we will expand the method section with an explicit description of the reference-free inference pipeline and report additional ROUGE scores obtained under that setting to demonstrate generalization. revision: yes

  2. Referee: [Experiments] No baselines, ablation studies, error analysis, or comparisons to other methods are reported despite the claim of effectiveness for the hierarchical strategy. Without these, the ROUGE2-F1 score of 0.2468 cannot be contextualized and the contribution of the proposed shortening method remains unverifiable.

    Authors: We agree that the lack of baselines and ablations limits the ability to assess the contribution of the shortening strategy. The revised manuscript will include (i) a baseline hierarchical BART system without the proposed shortening, (ii) ablation results isolating the effect of golden-summary-driven shortening, and (iii) an error analysis of generated summaries. Where possible we will also compare against other published Vietnamese summarization approaches. revision: yes

Circularity Check

0 steps flagged

Empirical ROUGE score from hierarchical BART pipeline with no self-referential derivation

full rationale

The paper reports an empirical test-set ROUGE2-F1 of 0.2468 obtained by training and evaluating a BART-based hierarchical model on the VLSP dataset. The shortening strategy is a training-time preprocessing step that uses reference summaries to select sentences; the final reported metric is measured on held-out test documents without access to those references. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the derivation. The result is therefore a standard empirical measurement rather than a quantity forced by construction from the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on standard assumptions that BART can be fine-tuned for summarization and that hierarchical condensation improves multi-document performance; no free parameters or invented entities are specified in the abstract.

axioms (2)
  • domain assumption BART can be effectively fine-tuned for abstractive summarization in Vietnamese
    Implicit in the choice of BART as the base model for the task.
  • domain assumption Hierarchical condensation followed by aggregation produces better multi-document summaries than direct methods
    Stated as the chosen popular approach without justification in the abstract.

pith-pipeline@v0.9.1-grok · 5660 in / 1267 out tokens · 29575 ms · 2026-06-26T20:37:15.841476+00:00 · methodology

0 comments
read the original abstract

In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022. We choose to follow the popular hierarchical approach, i.e. condensing each document followed by aggregation and summarization. We propose a novel yet simple strategy to shorten documents that is driven by the golden summary, thus ensuring high correlation between stages of the hierarchical approach. Our method achieves a ROUGE2-F1 score of 0.2468 on the VLSP's public test set, and can produce fluent and concise summaries. Additionally, we utilize external sources for extra data, which greatly enhances the quantity of data for Vietnamese multi-document summarization. The additional data is made available for the community.

Figures

Figures reproduced from arXiv: 2606.19591 by Huy Ngo Quang, Vu Nguyen Nguyen Xuan.

Figure 2
Figure 2. Figure 2: Flow of inference. contains ~56,000 clusters. Therefore, we decide to translate the Multinews dataset to Vietnamese and combine this translated set with the Vietnamese ones. In the end, we have a total ~51,000 clusters in our final training set. 3.2 Hyperparameters As mentioned, for two steps of sentence selection and summary generation, we fine-tune two distinct BARTPho (Tran et al., 2021) models. For bot… view at source ↗
Figure 1
Figure 1. Figure 1: An example of the training objective of the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

discussion (0)

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Reference graph

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